<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/34292?offset=60 https://bioinformaticsonline.com/bookmarks/view/39875/lrsday-long-read-sequencing-data-analysis-for-yeasts Mon, 26 Aug 2019 18:07:33 -0500 https://bioinformaticsonline.com/bookmarks/view/39875/lrsday-long-read-sequencing-data-analysis-for-yeasts <![CDATA[LRSDAY: Long-read Sequencing Data Analysis for Yeasts]]> Long-read sequencing technologies have become increasingly popular in genome projects due to their strengths in resolving complex genomic regions. As a leading model organism with small genome size and great biotechnological importance, the budding yeast, Saccharomyces cerevisiae, has many isolates currently being sequenced with long reads. 

Address of the bookmark: https://github.com/yjx1217/LRSDAY

]]> Poonam Mahapatra https://bioinformaticsonline.com/news/view/42324/comparative-genomics-data-set-including-240-mammals-released Thu, 19 Nov 2020 06:45:39 -0600 https://bioinformaticsonline.com/news/view/42324/comparative-genomics-data-set-including-240-mammals-released <![CDATA[Comparative Genomics Data Set Including 240 Mammals Released !]]> The genome of 130 mammals was sequenced by a large international consortium and the data was analyzed together with 110 existing genomes to allow scientists to identify the important positions in the DNA. This report, published in Nature today will help advance research on human disease mutations and inform how best to protect endangered species.

In addition to the knowledge of the human genome, all these genomes, widely sampled across mammals, can be used to research how particular organisms respond to different conditions. Some otters, for example, have a thick, water-resistant shell, and some rodents, but not all, have adapted to hibernation. These animal traits will help us to understand human traits, such as metabolic diseases.

image

With climate change and more animal ecosystems being threatened by human activity, the protection of endangered species is becoming increasingly important. Scientists have historically researched several people in various populations of a species to understand the genetic variation that occurs in that species. This is important for understanding how particular species can be protected. In this study, animals on the Red List of Endangered Species of the International Union for Conservation of Nature had fewer differences in their genomes, which is consistent with their endangered status.

Ref @ A comparative genomics multitool for scientific discovery and conservation https://www.nature.com/articles/s41586-020-2876-6

 Data at http://zoonomiaproject.org/

]]> Jit https://bioinformaticsonline.com/bookmarks/view/44545/amr-database Tue, 04 Jun 2024 13:37:21 -0500 https://bioinformaticsonline.com/bookmarks/view/44545/amr-database <![CDATA[AMR Database !]]>
  • ARG-ANNOT. PMID: 24145532
  • CARD. PMID: 23650175
  • MEGARes PMID: 27899569
  • NCBI BioProject: PRJNA313047
  • plasmidfinder PMID: 24777092
  • resfinder. PMID: 22782487
  • VFDB. PMID: 26578559
  • SRST2's version of ARG-ANNOT. PMID: 25422674.
  • VirulenceFinder PMID: 24574290.

    • Address of the bookmark: https://github.com/sanger-pathogens/ariba/wiki/Task%3A-getref

    ]]>     LEGE     https://bioinformaticsonline.com/blog/view/44852/what-is-data-science-%E2%80%94-a-bioinformatics-perspective Mon, 16 Jun 2025 01:44:34 -0500 https://bioinformaticsonline.com/blog/view/44852/what-is-data-science-%E2%80%94-a-bioinformatics-perspective <![CDATA[What is Data Science? — A Bioinformatics Perspective]]> In today’s era of big biology, we’re generating more data than ever before—genomes, transcriptomes, proteomes, metabolomes, microbiomes… you name it. But raw biological data doesn’t speak for itself. Making sense of it requires more than traditional biology. This is where data science steps in.

    So, What Is Data Science?
    At its core, data science is the interdisciplinary field that extracts knowledge and insights from data using programming, statistics, and domain expertise. In bioinformatics, data science enables us to turn gigabytes of sequence data into biological meaning.

    Imagine trying to understand gene regulation in cancer by analyzing thousands of RNA-seq samples, or predicting antibiotic resistance from bacterial genomes—these challenges are not solvable through wet lab experiments alone. They require data-driven thinking.

    Data Science Meets Bioinformatics
    Bioinformatics is inherently a data science domain. From genomics to systems biology, every field in modern biology relies on data science techniques to:

    Clean and process massive datasets

    Discover patterns in high-dimensional data

    Build predictive models (e.g., for disease classification)

    Visualize complex biological networks and trends

    Integrate diverse data types (e.g., transcriptomic + epigenomic data)

    The Bioinformatics Toolkit
    Here’s what data science typically looks like in bioinformatics:

    Task Data Science Role
    Sequence alignment Efficient algorithms, indexing, parallel processing
    Gene expression analysis Statistical modeling (e.g., DESeq2, limma)
    Variant calling Data filtering, probabilistic models
    Clustering of cells in single-cell data Unsupervised learning
    Protein structure prediction Deep learning models (e.g., AlphaFold)
    Metagenomics Data integration, classification, dimensionality reduction

    Common tools include Python, R, Bioconductor, scikit-learn, Pandas, Seurat, and TensorFlow—often working together in reproducible workflows.

    It's Not Just About Coding
    A common misconception is that bioinformatics is just programming or scripting. But being a data scientist in bioinformatics also means:

    Understanding experimental design

    Asking biologically meaningful questions

    Choosing the right statistical or machine learning models

    Communicating findings effectively (e.g., plots, dashboards, papers)

    In other words, data science in bioinformatics is where biology, statistics, and computer science converge.

    Why It Matters
    The real power of data science in bioinformatics is its ability to scale discovery.

    Instead of studying one gene, we can study thousands.

    Instead of analyzing one species, we can explore entire ecosystems.

    Instead of waiting months for lab results, we can generate hypotheses in days.

    From personalized medicine and cancer diagnostics to agricultural genomics and pandemic surveillance, data science is at the heart of the bioinformatics revolution.

    Final Thoughts
    If you’re a biologist who’s curious about code, or a data enthusiast fascinated by life sciences, bioinformatics is your playground—and data science is your toolkit.

    In bioinformatics, data science isn’t just useful. It’s essential.

     

    ]]>     Abhi     https://bioinformaticsonline.com/bookmarks/view/34235/quorum-an-error-corrector-for-illumina-reads Wed, 08 Nov 2017 11:40:41 -0600 https://bioinformaticsonline.com/bookmarks/view/34235/quorum-an-error-corrector-for-illumina-reads <![CDATA[QuorUM: An Error Corrector for Illumina Reads]]> Illumina Sequencing data can provide high coverage of a genome by relatively short (most often 100 bp to 150 bp) reads at a low cost. Even with low (advertised 1%) error rate, 100 × coverage Illumina data on average has an error in some read at every base in the genome. These errors make handling the data more complicated because they result in a large number of low-count erroneous k-mers in the reads. However, there is enough information in the reads to correct most of the sequencing errors, thus making subsequent use of the data (e.g. for mapping or assembly) easier. Here we use the term “error correction” to denote the reduction in errors due to both changes in individual bases and trimming of unusable sequence. We developed an error correction software called QuorUM. QuorUM is mainly aimed at error correcting Illumina reads for subsequent assembly. It is designed around the novel idea of minimizing the number of distinct erroneous k-mers in the output reads and preserving the most true k-mers, and we introduce a composite statistic π that measures how successful we are at achieving this dual goal. We evaluate the performance of QuorUM by correcting actual Illumina reads from genomes for which a reference assembly is available.

    QuorUM is distributed as an independent software package and as a module of the MaSuRCA assembly software. Both are available under the GPL open source license at http://www.genome.umd.edu.

    Address of the bookmark: http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0130821

    ]]>     Jit     https://bioinformaticsonline.com/bookmarks/view/40893/quorum-an-error-corrector-for-illumina-reads Tue, 04 Feb 2020 23:26:55 -0600 https://bioinformaticsonline.com/bookmarks/view/40893/quorum-an-error-corrector-for-illumina-reads <![CDATA[QuorUM: An Error Corrector for Illumina Reads]]> We produce trimmed and error-corrected reads that result in assemblies with longer contigs and fewer errors. We compared QuorUM against several published error correctors and found that it is the best performer in most metrics we use. QuorUM is efficiently implemented making use of current multi-core computing architectures and it is suitable for large data sets (1 billion bases checked and corrected per day per core)

    Address of the bookmark: http://www.genome.umd.edu/

    ]]>     BioStar     https://bioinformaticsonline.com/bookmarks/view/37473/lsc-a-long-read-error-correction-tool Thu, 02 Aug 2018 07:39:46 -0500 https://bioinformaticsonline.com/bookmarks/view/37473/lsc-a-long-read-error-correction-tool <![CDATA[LSC :a long read error correction tool]]> Getting Started

    These simple steps will help you integrate LSC into your transcriptomics analysis pipeline.

    • Read the LSC_requirements for running LSC.
    • Download and set-up the LSC package.
    • Follow the tutorial to see how LSC works on some example data.
    • Read the manual if anything is unclear.
    • You're ready, Happy LSCing!

    Latest publication

    Kin Fai Au, Jason Underwood, Lawrence Lee and Wing Hung Wong 
    Improving PacBio Long Read Accuracy by Short Read Alignment [Manuscript] 
    PLoS ONE 2012. 7(10): e46679. doi:10.1371/journal.pone.0046679

    Address of the bookmark: https://www.healthcare.uiowa.edu/labs/au/LSC/

    ]]>     Jit     https://bioinformaticsonline.com/bookmarks/view/38792/nxrepair-error-correction-in-de-novo-assemblies-using-nextera-mate-pair-reads Thu, 24 Jan 2019 10:35:12 -0600 https://bioinformaticsonline.com/bookmarks/view/38792/nxrepair-error-correction-in-de-novo-assemblies-using-nextera-mate-pair-reads <![CDATA[NxRepair: error correction in de novo assemblies using Nextera Mate Pair Reads]]> NxRepair is a python module that automatically detects large structural errors in de novo assemblies using Nextera mate pair reads. The decector will break a contig at the site of an identified misassembly and will generate a new fasta file containing both the corrected contigs and the correct, unaffected contigs.

    https://nxrepair.readthedocs.io/en/latest/tutorial.html

    nxrepair aligned_matepairs.bam assemblyfasta.fasta error_locations.csv new_fasta.fasta
     

    Address of the bookmark: https://github.com/rebeccaroisin/nxrepair

    ]]>     BioStar     https://bioinformaticsonline.com/bookmarks/view/32131/wgs-celera-assembler-version-83rc2 Mon, 10 Apr 2017 04:45:40 -0500 https://bioinformaticsonline.com/bookmarks/view/32131/wgs-celera-assembler-version-83rc2 <![CDATA[WGS Celera Assembler version 8.3rc2]]> These are release notes for Celera Assembler version 8.3rc2, which was released on May 24, 2015.

    This distribution package provides a stable, tested, documented version of the software.  The distribution is usable on most Unix-like platforms, and some platforms have pre-compiled binary distributions ready for installation.

    The source code package includes full source code (revision 4627), Makefiles, and scripts.  A subset of the kmer package (http://kmer.sourceforge.net/, version r1994), used by some modules of Celera Assembler, is included.  This distribution includes [http://samtools.sourceforge.net/ SAMtools], [http://www.cbcb.umd.edu/software/jellyfish/ Jellyfish 2.0], [https://github.com/pbjd/pbutgcns PBUTGCNS], [https://github.com/PacificBiosciences/pbdagcon PBDAGCON], [https://github.com/PacificBiosciences/BLASR BLASR], and parts of the [https://github.com/PacificBiosciences/FALCON/tree/v0.1.3 Falcon assembler].

    Full documentation can be found online at http://wgs-assembler.sourceforge.net/.

    Interesting scripts within it

    urbe@urbo214b[bin] ls                                                        []
    -rwxrwxr-x 1 urbe urbe  11K Apr 10 11:41 addCNSToStore
    -rwxrwxr-x 1 urbe urbe 575K Apr 10 11:41 addReadsToUnitigs
    -rwxrwxr-x 1 urbe urbe 128K Apr 10 11:41 analyzeBest
    -rwxrwxr-x 1 urbe urbe 257K Apr 10 11:41 analyzePosMap
    -rwxrwxr-x 1 urbe urbe 1,5M Apr 10 11:41 analyzeScaffolds
    -rwxrwxr-x 1 urbe urbe 224K Apr 10 11:41 asmOutputFasta
    -rwxrwxr-x 1 urbe urbe 448K Apr 10 11:41 asmOutputStatistics
    -rwxrwxr-x 1 urbe urbe 2,4K Apr 10 11:41 asmToAGP.pl
    -rwxrwxr-x 1 urbe urbe 7,6M Apr 10 11:41 blasr
    -rwxrwxr-x 1 urbe urbe 1,6M Apr 10 11:41 bogart
    -rwxrwxr-x 1 urbe urbe 183K Apr 10 11:41 bogus
    -rwxrwxr-x 1 urbe urbe 272K Apr 10 11:41 bogusness
    -rwxrwxr-x 1 urbe urbe 247K Apr 10 11:41 buildPosMap
    -rwxrwxr-x 1 urbe urbe 213K Apr 10 11:41 buildRefContigs
    -rwxrwxr-x 1 urbe urbe 990K Apr 10 11:41 buildUnitigs
    -rwxrwxr-x 1 urbe urbe  18K Apr 10 11:41 ca2ace.pl
    -rwxrwxr-x 1 urbe urbe  12K Apr 10 11:41 caqc_help.ini
    -rwxrwxr-x 1 urbe urbe  61K Apr 10 11:41 caqc.pl
    -rwxrwxr-x 1 urbe urbe  23K Apr 10 11:41 cat-corrects
    -rwxrwxr-x 1 urbe urbe  24K Apr 10 11:41 cat-erates
    -rwxrwxr-x 1 urbe urbe 1,9M Apr 10 11:41 cgw
    -rwxrwxr-x 1 urbe urbe 1,4M Apr 10 11:41 cgwDump
    -rwxrwxr-x 1 urbe urbe 204K Apr 10 11:41 chimChe
    -rwxrwxr-x 1 urbe urbe 201K Apr 10 11:40 chimera
    -rwxrwxr-x 1 urbe urbe 220K Apr 10 11:41 classifyMates
    -rwxrwxr-x 1 urbe urbe 201K Apr 10 11:41 classifyMatesApply
    -rwxrwxr-x 1 urbe urbe 215K Apr 10 11:41 classifyMatesPairwise
    -rwxrwxr-x 1 urbe urbe 366K Apr 10 11:41 computeCoverageStat
    -rwxrwxr-x 1 urbe urbe 9,8K Apr 10 11:41 convert-fasta-to-v2.pl
    -rwxrwxr-x 1 urbe urbe  48K Apr 10 11:41 convertOverlap
    -rwxrwxr-x 1 urbe urbe 119K Apr 10 11:41 convertSamToCA
    -rwxrwxr-x 1 urbe urbe  20K Apr 10 11:41 convertToPBCNS
    -rwxrwxr-x 1 urbe urbe 197K Apr 10 11:41 correct-frags
    -rwxrwxr-x 1 urbe urbe 259K Apr 10 11:41 correct-olaps
    -rwxrwxr-x 1 urbe urbe 520K Apr 10 11:41 correctPacBio
    -rwxrwxr-x 1 urbe urbe 540K Apr 10 11:41 ctgcns
    -rwxrwxr-x 1 urbe urbe 162K Apr 10 11:40 deduplicate
    -rwxrwxr-x 1 urbe urbe  37K Apr 10 11:41 demotePosMap
    -rwxrwxr-x 1 urbe urbe 1,5M Apr 10 11:41 dumpCloneMiddles
    -rwxrwxr-x 1 urbe urbe 124K Apr 10 11:41 dumpPBRLayoutStore
    -rwxrwxr-x 1 urbe urbe 1,3M Apr 10 11:41 dumpSingletons
    -rwxrwxr-x 1 urbe urbe 171K Apr 10 11:41 erate-estimate
    -rwxrwxr-x 1 urbe urbe 221K Apr 10 11:40 estimate-mer-threshold
    -rwxrwxr-x 1 urbe urbe 1,5M Apr 10 11:41 extendClearRanges
    -rwxrwxr-x 1 urbe urbe 1,3M Apr 10 11:41 extendClearRangesPartition
    -rwxrwxr-x 1 urbe urbe 205K Apr 10 11:40 extractmessages
    -rwxrwxr-x 1 urbe urbe 7,2M Apr 10 11:41 falcon_sense
    -rwxrwxr-x 1 urbe urbe 9,8K Apr 10 11:41 fastaToCA
    -rwxrwxr-x 1 urbe urbe 124K Apr 10 11:40 fastqAnalyze
    -rwxrwxr-x 1 urbe urbe 137K Apr 10 11:40 fastqSample
    -rwxrwxr-x 1 urbe urbe  62K Apr 10 11:40 fastqSimulate
    -rwxrwxr-x 1 urbe urbe 121K Apr 10 11:40 fastqSimulate-sort
    -rwxrwxr-x 1 urbe urbe 246K Apr 10 11:40 fastqToCA
    -rwxrwxr-x 1 urbe urbe 140K Apr 10 11:41 filterOverlap
    -rwxrwxr-x 1 urbe urbe 341K Apr 10 11:40 finalTrim
    -rwxrwxr-x 1 urbe urbe 228K Apr 10 11:41 fixUnitigs
    -rwxrwxr-x 1 urbe urbe 147K Apr 10 11:40 fragmentDepth
    -rwxrwxr-x 1 urbe urbe  29K Apr 10 11:41 fragsInVars
    -rwxrwxr-x 1 urbe urbe 545K Apr 10 11:41 frgs2clones
    -rwxrwxr-x 1 urbe urbe 398K Apr 10 11:40 gatekeeper
    -rwxrwxr-x 1 urbe urbe 139K Apr 10 11:40 gatekeeperbench
    -rwxrwxr-x 1 urbe urbe 167K Apr 10 11:40 gkpStoreCreate
    -rwxrwxr-x 1 urbe urbe 147K Apr 10 11:40 gkpStoreDumpFASTQ
    -rwxrwxr-x 1 urbe urbe 184K Apr 10 11:41 greedyFragmentTiling
    -rwxrwxr-x 1 urbe urbe 1,6K Apr 10 11:41 greedy_layout_to_IUM
    -rwxrwxr-x 1 urbe urbe 142K Apr 10 11:40 initialTrim
    -rwxrwxr-x 1 urbe urbe 967K Apr 10 11:41 jellyfish
    -rwxrwxr-x 1 urbe urbe 219K Apr 10 11:41 markRepeatUnique
    -rwxrwxr-x 1 urbe urbe 273K Apr 10 11:40 markUniqueUnique
    -rwxrwxr-x 1 urbe urbe 114K Apr 10 11:40 mercy
    -rwxrwxr-x 1 urbe urbe 3,8K Apr 10 11:41 mergeqc.pl
    -rwxrwxr-x 1 urbe urbe 422K Apr 10 11:40 merTrim
    -rwxrwxr-x 1 urbe urbe 125K Apr 10 11:40 merTrimApply
    -rwxrwxr-x 1 urbe urbe 376K Apr 10 11:40 meryl
    -rwxrwxr-x 1 urbe urbe 176K Apr 10 11:41 metagenomics_ovl_analyses
    -rwxrwxr-x 1 urbe urbe 297K Apr 10 11:41 olap-from-seeds
    -rwxrwxr-x 1 urbe urbe 275K Apr 10 11:41 outputLayout
    -rwxrwxr-x 1 urbe urbe 229K Apr 10 11:41 overlapInCore
    -rwxrwxr-x 1 urbe urbe 144K Apr 10 11:40 overlap_partition
    -rwxrwxr-x 1 urbe urbe 179K Apr 10 11:41 overlapStats
    -rwxrwxr-x 1 urbe urbe 179K Apr 10 11:41 overlapStore
    -rwxrwxr-x 1 urbe urbe 153K Apr 10 11:41 overlapStoreBucketizer
    -rwxrwxr-x 1 urbe urbe 175K Apr 10 11:41 overlapStoreBuild
    -rwxrwxr-x 1 urbe urbe  33K Apr 10 11:41 overlapStoreIndexer
    -rwxrwxr-x 1 urbe urbe  48K Apr 10 11:41 overlapStoreSorter
    -rwxrwxr-x 1 urbe urbe 604K Apr 10 11:40 overmerry
    lrwxrwxrwx 1 urbe urbe    4 Apr 10 11:41 pacBioToCA -> PBcR
    -rwxrwxr-x 1 urbe urbe 131K Apr 10 11:41 PBcR
    -rwxrwxr-x 1 urbe urbe 2,9M Apr 10 11:41 pbdagcon
    -rwxrwxr-x 1 urbe urbe 1,9M Apr 10 11:41 pbutgcns
    -rwxrwxr-x 1 urbe urbe 201K Apr 10 11:40 remove_fragment
    -rwxrwxr-x 1 urbe urbe 153K Apr 10 11:40 removeMateOverlap
    -rwxrwxr-x 1 urbe urbe 2,5K Apr 10 11:41 replaceUIDwithName-fastq
    -rwxrwxr-x 1 urbe urbe 1,2K Apr 10 11:41 replaceUIDwithName-posmap
    -rwxrwxr-x 1 urbe urbe 1,3M Apr 10 11:41 resolveSurrogates
    -rwxrwxr-x 1 urbe urbe 139K Apr 10 11:41 rewriteCache
    -rwxrwxr-x 1 urbe urbe 232K Apr 10 11:41 runCA
    -rwxrwxr-x 1 urbe urbe  88K Apr 10 11:41 runCA-dedupe
    -rwxrwxr-x 1 urbe urbe  14K Apr 10 11:41 runCA-overlapStoreBuild
    -rwxrwxr-x 1 urbe urbe 3,6K Apr 10 11:41 run_greedy.csh
    -rwxrwxr-x 1 urbe urbe 297K Apr 10 11:40 sffToCA
    -rwxrwxr-x 1 urbe urbe  13K Apr 10 11:40 show-corrects
    -rwxrwxr-x 1 urbe urbe 557K Apr 10 11:41 splitUnitigs
    -rwxrwxr-x 1 urbe urbe 1,4M Apr 10 11:41 terminator
    drwxrwxr-x 2 urbe urbe 4,0K Apr 10 11:41 TIGR
    -rwxrwxr-x 1 urbe urbe 526K Apr 10 11:41 tigStore
    -rwxrwxr-x 1 urbe urbe  35K Apr 10 11:41 tracearchiveToCA
    -rwxrwxr-x 1 urbe urbe  35K Apr 10 11:41 tracedb-to-frg.pl
    -rwxrwxr-x 1 urbe urbe  44K Apr 10 11:41 trimFastqByQVWindow
    -rwxrwxr-x 1 urbe urbe  18K Apr 10 11:40 uidclient
    -rwxrwxr-x 1 urbe urbe 589K Apr 10 11:41 unitigger
    -rwxrwxr-x 1 urbe urbe  42K Apr 10 11:40 upgrade-v8-to-v9
    -rwxrwxr-x 1 urbe urbe  42K Apr 10 11:40 upgrade-v9-to-v10
    -rwxrwxr-x 1 urbe urbe  854 Apr 10 11:41 utg2fasta
    -rwxrwxr-x 1 urbe urbe 731K Apr 10 11:41 utgcns
    -rwxrwxr-x 1 urbe urbe 561K Apr 10 11:41 utgcnsfix


    Address of the bookmark: http://wgs-assembler.sourceforge.net/wiki/index.php/Main_Page

    ]]>     Jit     https://bioinformaticsonline.com/pages/view/35144/converting-fastq-to-fasta Fri, 12 Jan 2018 03:49:09 -0600 https://bioinformaticsonline.com/pages/view/35144/converting-fastq-to-fasta <![CDATA[Converting FASTQ to FASTA]]>

    There are several ways you can convert fastq to fasta sequences. Some methods are listed below.

    Using SED

    sed can be used to selectively print the desired lines from a file, so if you print the first and 2rd line of every 4 lines, you get the sequence header and sequence needed for fasta format.

    sed -n '1~4s/^@/>/p;2~4p' INFILE.fastq > OUTFILE.fasta

    Using PASTE

    You can linerize every 4 lines in a tabular format and print first and second field using paste

    cat INFILE.fastq | paste - - - - |cut -f 1, 2| sed 's/@/>/'g | tr -s "/t" "/n" > OUTFILE.fasta

    EMBOSS:seqret

    Standard script that can be used for many purposes. One such use is fastq-fasta conversion

    seqret -sequence reads.fastq -outseq reads.fasta

    awk can be used for conversion as follows:

    Using AWK

    cat infile.fq | awk '{if(NR%4==1) {printf(">%s\n",substr($0,2));} else if(NR%4==2) print;}' > file.fa

    FASTX-toolkit

    fastq_to_fasta is available in the FASTX-toolkit that scales really well with the huge datasets

    fastq_to_fasta -h
usage: fastq_to_fasta [-h] [-r] [-n] [-v] [-z] [-i INFILE] [-o OUTFILE]
# Remember to use -Q33 for illumina reads!
version 0.0.6
       [-h]         = This helpful help screen.
       [-r]         = Rename sequence identifiers to numbers.
       [-n]         = keep sequences with unknown (N) nucleotides.
                   Default is to discard such sequences.
       [-v]         = Verbose - report number of sequences.
                   If [-o] is specified,  report will be printed to STDOUT.
                   If [-o] is not specified (and output goes to STDOUT),
                   report will be printed to STDERR.
       [-z]         = Compress output with GZIP.
       [-i INFILE]  = FASTA/Q input file. default is STDIN.
       [-o OUTFILE] = FASTA output file. default is STDOUT.

    Bioawk

    Another option to convert fastq to fasta format using bioawk

    bioawk -c fastx '{print ">"$name"\n"$seq}' input.fastq > output.fasta

    Seqtk

    From the same developer, there is another option using a tool called seqtk

    seqtk seq -a input.fastq > output.fasta

    Note that you can use either compressed or uncompressed files for this tool

    ]]>     Neel